Overview

Dataset statistics

Number of variables33
Number of observations1460
Missing cells348
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory376.5 KiB
Average record size in memory264.1 B

Variable types

Numeric25
Categorical8

Alerts

MSSubClass is highly overall correlated with HalfBathHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
OverallQual is highly overall correlated with YearBuilt and 5 other fieldsHigh correlation
YearBuilt is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
YearRemodAdd is highly overall correlated with OverallQual and 3 other fieldsHigh correlation
1stFlrSF is highly overall correlated with SalePriceHigh correlation
2ndFlrSF is highly overall correlated with GrLivArea and 2 other fieldsHigh correlation
GrLivArea is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with 2ndFlrSF and 3 other fieldsHigh correlation
GarageYrBlt is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
GarageArea is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
SalePrice is highly overall correlated with OverallQual and 7 other fieldsHigh correlation
HalfBath is highly overall correlated with MSSubClassHigh correlation
GarageCars is highly overall correlated with GarageAreaHigh correlation
BsmtHalfBath is highly imbalanced (79.7%)Imbalance
KitchenAbvGr is highly imbalanced (85.7%)Imbalance
LotFrontage has 259 (17.7%) missing valuesMissing
GarageYrBlt has 81 (5.5%) missing valuesMissing
MiscVal is highly skewed (γ1 = 24.47679419)Skewed
MasVnrArea has 861 (59.0%) zerosZeros
2ndFlrSF has 829 (56.8%) zerosZeros
LowQualFinSF has 1434 (98.2%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
WoodDeckSF has 761 (52.1%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
EnclosedPorch has 1252 (85.8%) zerosZeros
3SsnPorch has 1436 (98.4%) zerosZeros
ScreenPorch has 1344 (92.1%) zerosZeros
PoolArea has 1453 (99.5%) zerosZeros
MiscVal has 1408 (96.4%) zerosZeros

Reproduction

Analysis started2023-10-14 18:47:11.816864
Analysis finished2023-10-14 18:49:49.301767
Duration2 minutes and 37.48 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

MSSubClass
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:49.628565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.300571
Coefficient of variation (CV)0.74345532
Kurtosis1.580188
Mean56.89726
Median Absolute Deviation (MAD)30
Skewness1.4076567
Sum83070
Variance1789.3383
MonotonicityNot monotonic
2023-10-14T21:49:50.052664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 536
36.7%
60 299
20.5%
50 144
 
9.9%
120 87
 
6.0%
30 69
 
4.7%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
20 536
36.7%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 299
20.5%
70 60
 
4.1%
75 16
 
1.1%
80 58
 
4.0%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 87
 
6.0%
90 52
 
3.6%
85 20
 
1.4%
80 58
 
4.0%
75 16
 
1.1%
70 60
 
4.1%
60 299
20.5%

LotFrontage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:51.615645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.284752
Coefficient of variation (CV)0.3466776
Kurtosis17.452867
Mean70.049958
Median Absolute Deviation (MAD)11
Skewness2.1635691
Sum84130
Variance589.74917
MonotonicityNot monotonic
2023-10-14T21:49:52.119477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (100) 654
44.8%
(Missing) 259
 
17.7%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

LotArea
Real number (ℝ)

HIGH CORRELATION 

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:52.616218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2023-10-14T21:49:53.035443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

OverallQual
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:53.356376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2023-10-14T21:49:53.790121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%
5 397
27.2%
4 116
 
7.9%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5753425
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:54.132579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1127993
Coefficient of variation (CV)0.199593
Kurtosis1.1064135
Mean5.5753425
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum8140
Variance1.2383224
MonotonicityNot monotonic
2023-10-14T21:49:54.388541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.0%
6 252
 
17.3%
5 821
56.2%
4 57
 
3.9%
3 25
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

YearBuilt
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:54.766600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2023-10-14T21:49:55.155908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:55.565015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2023-10-14T21:49:56.007905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

MasVnrArea
Real number (ℝ)

ZEROS 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.68526
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:56.445752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.06621
Coefficient of variation (CV)1.7463061
Kurtosis10.082417
Mean103.68526
Median Absolute Deviation (MAD)0
Skewness2.6690842
Sum150551
Variance32784.971
MonotonicityNot monotonic
2023-10-14T21:49:56.857161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 861
59.0%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
200 6
 
0.4%
Other values (317) 529
36.2%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 861
59.0%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

1stFlrSF
Real number (ℝ)

HIGH CORRELATION 

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:57.233026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2023-10-14T21:49:57.674150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.99247
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:58.052345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.52844
Coefficient of variation (CV)1.2580343
Kurtosis-0.55346356
Mean346.99247
Median Absolute Deviation (MAD)0
Skewness0.81302982
Sum506609
Variance190557.08
MonotonicityNot monotonic
2023-10-14T21:49:58.472117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 829
56.8%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
780 5
 
0.3%
Other values (407) 566
38.8%
ValueCountFrequency (%)
0 829
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8445205
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:58.825294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.623081
Coefficient of variation (CV)8.3194303
Kurtosis83.234817
Mean5.8445205
Median Absolute Deviation (MAD)0
Skewness9.0113413
Sum8533
Variance2364.204
MonotonicityNot monotonic
2023-10-14T21:49:59.136658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1434
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1434
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

HIGH CORRELATION 

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:49:59.505349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2023-10-14T21:49:59.921324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Length

2023-10-14T21:50:00.277331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:00.627564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

BsmtHalfBath
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Length

2023-10-14T21:50:01.069375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:01.328370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2023-10-14T21:50:01.653432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:01.933557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

HalfBath
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Length

2023-10-14T21:50:02.218889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:02.501117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

BedroomAbvGr
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8664384
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:02.747124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81577804
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean2.8664384
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum4185
Variance0.66549382
MonotonicityNot monotonic
2023-10-14T21:50:03.040695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 804
55.1%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 804
55.1%
4 213
 
14.6%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 804
55.1%
2 358
24.5%
1 50
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Length

2023-10-14T21:50:03.361554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:03.610223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

TotRmsAbvGrd
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5178082
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:03.855570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6253933
Coefficient of variation (CV)0.24937728
Kurtosis0.88076157
Mean6.5178082
Median Absolute Deviation (MAD)1
Skewness0.67634084
Sum9516
Variance2.6419033
MonotonicityNot monotonic
2023-10-14T21:50:04.174708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 402
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 402
27.5%
7 329
22.5%
8 187
12.8%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 75
 
5.1%
8 187
12.8%
7 329
22.5%
6 402
27.5%
5 275
18.8%
4 97
 
6.6%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Length

2023-10-14T21:50:04.480088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:04.728538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

GarageYrBlt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.5062
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:05.041458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.689725
Coefficient of variation (CV)0.012478973
Kurtosis-0.418341
Mean1978.5062
Median Absolute Deviation (MAD)21
Skewness-0.64941462
Sum2728360
Variance609.58251
MonotonicityNot monotonic
2023-10-14T21:50:05.373088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 65
 
4.5%
2006 59
 
4.0%
2004 53
 
3.6%
2003 50
 
3.4%
2007 49
 
3.4%
1977 35
 
2.4%
1998 31
 
2.1%
1999 30
 
2.1%
1976 29
 
2.0%
2008 29
 
2.0%
Other values (87) 949
65.0%
(Missing) 81
 
5.5%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
 
0.2%
1914 2
 
0.1%
1915 2
 
0.1%
1916 5
 
0.3%
1918 2
 
0.1%
1920 14
1.0%
1921 3
 
0.2%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 29
2.0%
2007 49
3.4%
2006 59
4.0%
2005 65
4.5%
2004 53
3.6%
2003 50
3.4%
2002 26
 
1.8%
2001 20
 
1.4%

GarageCars
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Length

2023-10-14T21:50:05.677265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:05.925495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:06.236435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2023-10-14T21:50:06.729119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

WoodDeckSF
Real number (ℝ)

ZEROS 

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.244521
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:07.267720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.33879
Coefficient of variation (CV)1.3299319
Kurtosis2.9929509
Mean94.244521
Median Absolute Deviation (MAD)0
Skewness1.5413758
Sum137597
Variance15709.813
MonotonicityNot monotonic
2023-10-14T21:50:07.672669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 761
52.1%
192 38
 
2.6%
100 36
 
2.5%
144 33
 
2.3%
120 31
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
208 10
 
0.7%
240 10
 
0.7%
Other values (264) 484
33.2%
ValueCountFrequency (%)
0 761
52.1%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%
635 1
0.1%
586 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%

OpenPorchSF
Real number (ℝ)

ZEROS 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.660274
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:08.025709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.256028
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean46.660274
Median Absolute Deviation (MAD)25
Skewness2.3643417
Sum68124
Variance4389.8612
MonotonicityNot monotonic
2023-10-14T21:50:08.541132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
36 29
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.5%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

EnclosedPorch
Real number (ℝ)

ZEROS 

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95411
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:09.056866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.119149
Coefficient of variation (CV)2.7839502
Kurtosis10.430766
Mean21.95411
Median Absolute Deviation (MAD)0
Skewness3.0898719
Sum32053
Variance3735.5503
MonotonicityNot monotonic
2023-10-14T21:50:09.542775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1252
85.8%
112 15
 
1.0%
96 6
 
0.4%
192 5
 
0.3%
144 5
 
0.3%
120 5
 
0.3%
216 5
 
0.3%
156 4
 
0.3%
116 4
 
0.3%
252 4
 
0.3%
Other values (110) 155
 
10.6%
ValueCountFrequency (%)
0 1252
85.8%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
34 2
 
0.1%
36 2
 
0.1%
37 1
 
0.1%
39 2
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%
294 1
0.1%
293 1
0.1%
291 1
0.1%
286 1
0.1%
280 1
0.1%

3SsnPorch
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:09.983628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.317331
Coefficient of variation (CV)8.5984939
Kurtosis123.66238
Mean3.409589
Median Absolute Deviation (MAD)0
Skewness10.304342
Sum4978
Variance859.50587
MonotonicityNot monotonic
2023-10-14T21:50:10.420261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1436
98.4%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1436
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%

ScreenPorch
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.060959
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:10.822875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.757415
Coefficient of variation (CV)3.7021159
Kurtosis18.439068
Mean15.060959
Median Absolute Deviation (MAD)0
Skewness4.1222137
Sum21989
Variance3108.8894
MonotonicityNot monotonic
2023-10-14T21:50:11.245232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1344
92.1%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1344
92.1%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%

PoolArea
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7589041
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:11.581846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.177307
Coefficient of variation (CV)14.562778
Kurtosis223.2685
Mean2.7589041
Median Absolute Deviation (MAD)0
Skewness14.828374
Sum4028
Variance1614.216
MonotonicityNot monotonic
2023-10-14T21:50:11.907616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1453
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1453
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
576 1
 
0.1%
648 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
738 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
519 1
 
0.1%
512 1
 
0.1%
480 1
 
0.1%
0 1453
99.5%

MiscVal
Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.489041
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:12.197243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.12302
Coefficient of variation (CV)11.408001
Kurtosis701.00334
Mean43.489041
Median Absolute Deviation (MAD)0
Skewness24.476794
Sum63494
Variance246138.06
MonotonicityNot monotonic
2023-10-14T21:50:12.511396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1408
96.4%
400 11
 
0.8%
500 8
 
0.5%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
15500 1
 
0.1%
Other values (11) 11
 
0.8%
ValueCountFrequency (%)
0 1408
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
480 2
 
0.1%
500 8
 
0.5%
560 1
 
0.1%
600 4
 
0.3%
620 1
 
0.1%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 1
 
0.1%
2000 4
0.3%
1400 1
 
0.1%
1300 1
 
0.1%
1200 2
0.1%
1150 1
 
0.1%
800 1
 
0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3219178
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:12.798931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7036262
Coefficient of variation (CV)0.42765918
Kurtosis-0.40410934
Mean6.3219178
Median Absolute Deviation (MAD)2
Skewness0.21205299
Sum9230
Variance7.3095947
MonotonicityNot monotonic
2023-10-14T21:50:13.092948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 253
17.3%
7 234
16.0%
5 204
14.0%
4 141
9.7%
8 122
8.4%
3 106
7.3%
10 89
 
6.1%
11 79
 
5.4%
9 63
 
4.3%
12 59
 
4.0%
Other values (2) 110
7.5%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 106
7.3%
4 141
9.7%
5 204
14.0%
6 253
17.3%
7 234
16.0%
8 122
8.4%
9 63
 
4.3%
10 89
 
6.1%
ValueCountFrequency (%)
12 59
 
4.0%
11 79
 
5.4%
10 89
 
6.1%
9 63
 
4.3%
8 122
8.4%
7 234
16.0%
6 253
17.3%
5 204
14.0%
4 141
9.7%
3 106
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Length

2023-10-14T21:50:13.490367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T21:50:13.776321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

SalePrice
Real number (ℝ)

HIGH CORRELATION 

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-14T21:50:14.178304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2023-10-14T21:50:14.588757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Interactions

2023-10-14T21:49:37.154663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:16.913386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:21.402450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:25.624917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:30.118176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:34.576701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:39.994695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-10-14T21:47:34.335230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:39.801982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:44.842357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:49.815216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:47:55.448642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:00.485776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:05.542319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:10.228407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:15.709138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:21.314208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:26.889406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:32.834003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:37.710505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:41.979683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:47.048004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:48:53.934927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:49:03.090812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:49:11.644438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:49:20.082621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:49:28.485499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T21:49:36.853861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-14T21:50:14.946211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
MSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrArea1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBedroomAbvGrTotRmsAbvGrdGarageYrBltGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldSalePriceBsmtFullBathBsmtHalfBathFullBathHalfBathKitchenAbvGrFireplacesGarageCarsYrSold
MSSubClass1.000-0.314-0.2700.108-0.0720.0360.0070.025-0.2780.4880.0760.2040.0690.1660.079-0.0470.0230.0320.011-0.036-0.0220.033-0.0330.0180.0070.2090.0860.2490.5120.4760.1930.2430.000
LotFrontage-0.3141.0000.6500.255-0.0830.1950.1170.2590.4280.055-0.0300.3760.3280.3660.1160.3780.1090.178-0.0960.0640.0440.0850.0240.0260.4090.1550.0000.1350.0330.0190.2500.1820.007
LotArea-0.2700.6501.0000.233-0.0470.1030.0750.1780.4440.119-0.0200.4490.3380.4060.0420.3670.1840.177-0.0670.0620.0920.0840.0590.0060.4560.2110.0000.0980.0000.0000.1600.0110.000
OverallQual0.1080.2550.2331.000-0.1780.6470.5580.4140.4090.290-0.0340.6030.1220.4280.6180.5420.2590.435-0.1620.0330.0460.057-0.0880.0610.8100.0660.0620.4040.2250.1060.2670.4020.000
OverallCond-0.072-0.083-0.047-0.1781.000-0.417-0.041-0.179-0.1670.0010.040-0.154-0.004-0.105-0.379-0.201-0.043-0.1330.1100.0320.075-0.0060.087-0.007-0.1290.0000.1020.3090.0790.0730.1050.2190.050
YearBuilt0.0360.1950.1030.647-0.4171.0000.6840.4020.2930.030-0.1460.288-0.0350.1770.8910.5280.2880.393-0.4090.022-0.0730.009-0.0920.0190.6530.1440.0890.3510.2270.2140.1690.3400.000
YearRemodAdd0.0070.1170.0750.558-0.0410.6841.0000.2340.2400.073-0.0650.282-0.0540.1980.7220.3980.2300.353-0.2350.052-0.0460.003-0.0910.0210.5710.1220.0770.2700.2000.1140.1360.2760.000
MasVnrArea0.0250.2590.1780.414-0.1790.4020.2341.0000.3520.063-0.1070.3230.1130.2640.3060.3650.1740.209-0.1800.0410.0380.005-0.0500.0180.4210.0280.0000.1820.1380.0000.1550.2010.040
1stFlrSF-0.2780.4280.4440.409-0.1670.2930.2400.3521.000-0.276-0.0390.4940.1410.3620.2300.4900.2190.235-0.1290.0600.1080.071-0.0330.0540.5750.1910.0000.2580.1210.0410.3410.2380.008
2ndFlrSF0.4880.0550.1190.2900.0010.0300.0730.063-0.2761.0000.0580.6430.5100.5870.0730.0980.0710.2250.046-0.0230.0120.061-0.0050.0430.2940.1250.0000.4070.4490.0000.1650.2280.054
LowQualFinSF0.076-0.030-0.020-0.0340.040-0.146-0.065-0.107-0.0390.0581.0000.0640.0210.042-0.028-0.048-0.0420.0100.0480.022-0.0190.0660.029-0.004-0.0680.0000.0000.0000.0000.0000.0000.0870.021
GrLivArea0.2040.3760.4490.603-0.1540.2880.2820.3230.4940.6430.0641.0000.5430.8280.2810.4680.2270.398-0.0490.0340.0860.068-0.0490.0810.7310.1360.0000.4680.3000.0000.3760.2870.042
BedroomAbvGr0.0690.3280.3380.122-0.004-0.035-0.0540.1130.1410.5100.0210.5431.0000.668-0.0560.1120.0560.1000.002-0.0190.0340.0720.0130.0510.2350.2550.0300.4480.2500.2330.1070.1340.021
TotRmsAbvGrd0.1660.3660.4060.428-0.1050.1770.1980.2640.3620.5870.0420.8280.6681.0000.1980.3310.1650.285-0.029-0.0030.0320.059-0.0210.0400.5330.0630.0000.3890.2700.1740.2230.2420.000
GarageYrBlt0.0790.1160.0420.618-0.3790.8910.7220.3060.2300.073-0.0280.281-0.0560.1981.0000.5920.2740.394-0.3170.017-0.100-0.007-0.0670.0120.5940.1320.0640.3380.1920.3090.1450.3880.000
GarageArea-0.0470.3780.3670.542-0.2010.5280.3980.3650.4900.098-0.0480.4680.1120.3310.5921.0000.2480.338-0.1780.0360.0290.042-0.0360.0330.6490.1370.0250.2790.1600.0920.2280.7590.000
WoodDeckSF0.0230.1090.1840.259-0.0430.2880.2300.1740.2190.071-0.0420.2270.0560.1650.2740.2481.0000.124-0.158-0.028-0.0900.0500.0170.0380.3540.1900.0000.2480.0820.0000.1460.1480.031
OpenPorchSF0.0320.1780.1770.435-0.1330.3930.3530.2090.2350.2250.0100.3980.1000.2850.3940.3380.1241.000-0.1690.0170.0070.037-0.0350.0660.4780.0790.0000.1660.1490.0000.1220.1290.000
EnclosedPorch0.011-0.096-0.067-0.1620.110-0.409-0.235-0.180-0.1290.0460.048-0.0490.002-0.029-0.317-0.178-0.158-0.1691.000-0.039-0.0810.0040.039-0.029-0.2180.0300.0490.1070.0780.0360.0430.2470.024
3SsnPorch-0.0360.0640.0620.0330.0320.0220.0520.0410.060-0.0230.0220.034-0.019-0.0030.0170.036-0.0280.017-0.0391.000-0.038-0.0090.0050.0370.0650.0000.0580.0000.0000.0000.0000.0000.000
ScreenPorch-0.0220.0440.0920.0460.075-0.073-0.0460.0380.1080.012-0.0190.0860.0340.032-0.1000.029-0.0900.007-0.081-0.0381.0000.0190.0150.0240.1000.0000.0000.0460.0460.0000.1240.0000.000
PoolArea0.0330.0850.0840.057-0.0060.0090.0030.0050.0710.0610.0660.0680.0720.059-0.0070.0420.0500.0370.004-0.0090.0191.0000.042-0.0230.0580.0990.0000.1060.0000.0000.1860.0000.000
MiscVal-0.0330.0240.059-0.0880.087-0.092-0.091-0.050-0.033-0.0050.029-0.0490.013-0.021-0.067-0.0360.017-0.0350.0390.0050.0150.0421.0000.011-0.0630.0000.0000.0000.1980.0850.0510.0320.021
MoSold0.0180.0260.0060.061-0.0070.0190.0210.0180.0540.043-0.0040.0810.0510.0400.0120.0330.0380.066-0.0290.0370.024-0.0230.0111.0000.0690.0000.0000.0440.0550.0200.0000.0000.155
SalePrice0.0070.4090.4560.810-0.1290.6530.5710.4210.5750.294-0.0680.7310.2350.5330.5940.6490.3540.478-0.2180.0650.1000.058-0.0630.0691.0000.1400.0490.4160.2080.0460.2890.4160.000
BsmtFullBath0.2090.1550.2110.0660.0000.1440.1220.0280.1910.1250.0000.1360.2550.0630.1320.1370.1900.0790.0300.0000.0000.0990.0000.0000.1401.0000.0970.2640.1520.1290.1120.1150.052
BsmtHalfBath0.0860.0000.0000.0620.1020.0890.0770.0000.0000.0000.0000.0000.0300.0000.0640.0250.0000.0000.0490.0580.0000.0000.0000.0000.0490.0971.0000.1640.1540.4980.0000.0720.024
FullBath0.2490.1350.0980.4040.3090.3510.2700.1820.2580.4070.0000.4680.4480.3890.3380.2790.2480.1660.1070.0000.0460.1060.0000.0440.4160.2640.1641.0000.2300.1130.1800.3290.000
HalfBath0.5120.0330.0000.2250.0790.2270.2000.1380.1210.4490.0000.3000.2500.2700.1920.1600.0820.1490.0780.0000.0460.0000.1980.0550.2080.1520.1540.2301.0000.1920.1640.1970.000
KitchenAbvGr0.4760.0190.0000.1060.0730.2140.1140.0000.0410.0000.0000.0000.2330.1740.3090.0920.0000.0000.0360.0000.0000.0000.0850.0200.0460.1290.4980.1130.1921.0000.0860.1230.000
Fireplaces0.1930.2500.1600.2670.1050.1690.1360.1550.3410.1650.0000.3760.1070.2230.1450.2280.1460.1220.0430.0000.1240.1860.0510.0000.2890.1120.0000.1800.1640.0861.0000.2020.030
GarageCars0.2430.1820.0110.4020.2190.3400.2760.2010.2380.2280.0870.2870.1340.2420.3880.7590.1480.1290.2470.0000.0000.0000.0320.0000.4160.1150.0720.3290.1970.1230.2021.0000.000
YrSold0.0000.0070.0000.0000.0500.0000.0000.0400.0080.0540.0210.0420.0210.0000.0000.0000.0310.0000.0240.0000.0000.0000.0210.1550.0000.0520.0240.0000.0000.0000.0300.0001.000

Missing values

2023-10-14T21:49:47.268464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-14T21:49:48.580047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-14T21:49:49.132167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrArea1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
06065.084507520032003196.085685401710102131802003.025480610000022008208500
12080.0960068197619760.01262001262012031611976.0246029800000052007181500
26068.0112507520012002162.092086601786102131612001.026080420000092008223500
37060.0955075191519700.096175601717101031711998.03642035272000022006140000
46084.0142608520002000350.01145105302198102141912000.038361928400000122008250000
55085.01411555199319950.079656601362101111501993.024804030032000700102009143000
62075.0100848520042005186.01694001694102031712004.02636255570000082007307000
760NaN103827619731973240.0110798302090102131721973.02484235204228000350112009200000
85051.0612075193119500.0102275201774002022821931.02468900205000042008129900
919050.0742056193919500.01077001077101022521939.01205040000012008118000
MSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrArea1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
14509060.0900055197419740.08968960179200224280NaN0032450000092009136000
14512078.092628520082009194.01578001578002031712008.038400360000052009287090
145218035.03675552005200580.01072001072101021502005.025250280000052006145000
14532090.01721755200620060.0114000114000103160NaN003656000007200684500
14542062.0750075200420050.01221001221102021602004.02400011300000102009185000
14556062.0791765199920000.095369401647002131711999.024600400000082007175000
14562085.0131756619781988119.02073002073102031721978.0250034900000022010210000
14577066.0904279194120060.01188115202340002041921941.012520600000250052010266500
14582068.0971756195019960.01078001078101021501950.012403660112000042010142125
14592075.0993756196519650.01256001256101131601965.01276736680000062008147500